Organoids / 3D: a pipeline architecture for trustworthy imaging.
01 · Phenotypic Discovery
Find meaningful biological responses across large experimental spaces.
High-content experiments generate more images than scientists can examine directly, but scale alone does not produce insight. The analysis must preserve the relationship between perturbations, controls, cells, wells, plates, and batches while turning millions of individual observations into comparable phenotypic profiles.
Fovea Lab supports the full path from image correction and cellular segmentation to feature extraction, representation learning, batch correction, and biological readout. Each result remains connected to the conditions and quality checks that produced it, allowing researchers to distinguish reproducible phenotypes from technical variation and investigate rare responses that would otherwise disappear inside population averages.
02 · Space & Time
Follow biological systems as they form, move, and change.
Volumetric and time-resolved imaging reveals processes that cannot be understood from a single field or endpoint. Cells migrate, tissues reorganize, structures develop, and rare events emerge across large image volumes. Extracting those events requires more than processing individual frames: the analysis must remain consistent across depth, time, scale, and changing image quality.
Fovea Lab brings acquisition context, restoration, segmentation, registration, tracking, and quantitative measurement into one inspectable workflow. Researchers can follow an observation from a rendered volume or trajectory back to the source data and processing decisions that created it, while quality checks expose where depth, motion, density, or signal loss may have changed the reliability of the result.
03 · Tissue & Spatial Biology
Connect cellular organization with molecular and pathological context.
Tissue images contain information at several scales at once: cellular morphology, local neighborhoods, larger anatomical structures, molecular measurements, and clinically meaningful regions. These signals are often processed in separate systems, making it difficult to understand how a final classification, spatial association, or tissue-level measurement was formed.
Fovea Lab structures image processing, registration, object detection, spatial measurement, and model interpretation as one traceable analysis. Imaging and molecular information can remain aligned through each transformation, while provenance, cohort-aware validation, and reviewable outputs help researchers examine whether a finding is consistent across samples, scanners, staining conditions, and experimental sites.
Pipeline architectures, module by module.
WHAT IT IS
Culture with depth
Self-organizing cultures reproduce tissue architecture that a flat monolayer cannot, and each object is measured as a volume rather than averaged across the well.
GET HELP WITH
Screening in depth
Dose-response and morphological classification from depth stacks, so a treatment that slows growth is separated from one that kills.
Pipeline backbone — reshapes per modality. Select a module for its sub-steps, tools, and deliverable.
Organoids / 3D
High confidenceIngest
Read multi-well confocal or light-sheet z-stack acquisitions for organoid, spheroid, or biofilm assays into a harmonized, chunked, analysis-ready representation with per-well and per-object indexing. The key challenges are proprietary vendor formats (CZI, LIF, ND2, TIFF series), large z-depth (50-200 planes), and correct physical calibration (voxel size XYZ, z-step).
Operations
- Enumerate acquisition filesWalk plate/well directory tree or OMERO dataset; build a file manifest mapping plate/well/field/z/channel to file paths.
- Read raw z-stacks via Bio-Formats / bioioLoad each z-stack preserving all planes, channels, and physical metadata (pixel size XY, z-step, objective, NA) using a format-aware reader.
- Validate axis order and calibrationAssert TCZYX axis order; verify z-step is non-zero and consistent across wells; flag wells where z-spacing differs from the acquisition protocol.
- Rechunk and write OME-ZarrWrite each well's z-stack as an OME-Zarr store (chunk size tuned for 3D access patterns, e.g. 1x1x32x512x512); build multiscale pyramid for large fields.
- Attach OMERO or plate metadataoptionalLink well-level annotations (condition, passage, matrix, timepoint) from the plate map; write OME-NGFF well/plate metadata.
Inputs
- Raw 3D z-stack acquisitions from confocal or light-sheet microscopeCZI · LIF · ND2 · OME-TIFF · TIFF series · LIF · VSI
- Plate map / assay manifestCSV · JSON · XLSX
Outputs
- Per-well harmonized 3D z-stacks with physical calibrationOME-Zarr · OME-TIFF
- Ingest manifest with file-to-well mapping and validation flagsJSON · Parquet
QC checks
- Incomplete z-stack detection — Flag wells where the number of z planes acquired is below the expected acquisition protocol value (e.g. < 70 of 80 expected planes).
- Z-step consistency — Check that z-step is uniform across all wells; flag outliers >5% deviation from the plate median (indicates stage drift or protocol error).
- Voxel calibration presence — Assert that physical pixel size (XY and Z) is non-null and non-zero in the OME-XML/NGFF metadata; fail the well if missing.
- Missing well completeness — Verify that all expected wells have corresponding image files; report missing wells as failed.
Failure modes it guards against
- Incorrect z-step written by vendor software — Cross-check OME-XML z-step against acquisition protocol; allow manual override in the ingest manifest.
- Axis order ambiguity (CZYX vs TCZYX) — Enforce explicit axis labeling via bioio/ome-zarr-py; add a unit test that reads back a known z-stack and asserts shape/axis.
- Partial z-stack (imaging stopped early) — Flag wells with n_z_planes < threshold; exclude or pad during preprocessing.
- Metadata stripped by file copy — Use Bio-Formats or bioio readers that preserve embedded OME-XML; never use plain TIFF export from the microscope without metadata.
Tools (9)
Read >150 proprietary microscopy formats (CZI, LIF, ND2, VSI, TIFF series); preserves all vendor metadata including z-step and objective.
Pluggable Python image IO framework for microscopy; successor to AICSImageIO with format-specific plugins (bioio-czi, bioio-ome-tiff, bioio-ome-zarr).
Convert, validate, and write OME-Zarr/OME-NGFF datasets including plate/well layout and multiscale pyramids.
- AICSImageIOOSS · BSD-3-Clause
Python image reader/writer for microscopy formats and OME-Zarr/TIFF workflows; predecessor to bioio, still widely used.
- zarrLibrary · MIT
Chunked, compressed N-dimensional array storage; backing store for OME-Zarr.
- tifffileLibrary · BSD-3-Clause
Read/write TIFF, BigTIFF, OME-TIFF and pyramidal image variants; handles large z-stacks with memory mapping.
- OMEROOSS · AGPL-3.0
Image data management, metadata, rendering, and access control for bioimaging facilities; enables programmatic ingest via OMERO.py.
- FractalOSS · BSD-3-Clause
Distributed bioimage workflow system centered on OME-Zarr plates; automates ingest + rechunking for HCS data.
- ImarisCommercial · commercial
Commercial 3D/4D image visualization and analysis platform; can ingest CZI/ND2 and export to HDF5/IMS format for large volumes.
Metadata captured
| Field | Type | Example | Standard |
|---|---|---|---|
| pipeline_run_id | string | organoids-ingest-2026-06-05-run01 | — |
| software_versions | object | {'bioio': '3.3.0', 'ome-zarr-py': '0.10.2', 'python': '3.11', 'container': 'sha256:abc123'} | REMBI |
| input_dataset_uri | uri | s3://client-study/plate01/raw/ | OME-NGFF |
| output_dataset_uri | uri | s3://client-study/plate01/zarr/plate01.zarr | OME-NGFF |
| plate_id | string | Plate_2026-05-12_001 | OME-NGFF |
| well_id | string | C07 | OME-NGFF |
| voxel_size_xyz_um | array (micrometer) | 0.325, 0.325, 2.0 | OME-XML |
| z_step_um | float (micrometer) | 2.0 | OME-XML |
| n_z_planes | int (count) | 80 | OME-XML |
| objective_magnification | float (x) | 10.0 | OME-XML |
| objective_na | float | 0.45 | OME-XML |
| channel_names | array | DAPI, GFP, Brightfield | OME-XML |
| acquisition_instrument | string | Zeiss LSM 900 confocal | REMBI |
| raw_file_format | enum | CZI | — |
You get
- Harmonized OME-Zarr plate store · OME-Zarr
- Ingest validation report · JSON / HTML
References
- OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies paper
- OME-NGFF specification spec
- REMBI: Recommended Metadata for Biological Images paper
- BioIO — image reading, metadata management, and image writing for microscopy docs
- Bio-Formats: a software tool suite for reading and converting file formats in biological imaging paper